What would you do with your microbiome sequence?

If you had access to free microbiome sequencing tests, to detect and analyze bacteria living in the nose, mouth, skin, gastro-intestinal, and/or urogenital areas of the body, what experiments would you think up?

Would you compare oral bacteria in people with lots of cavities vs. people with no cavities, look for differences between people with clear skin and acne, or sample your gut flora as you travel or change your diet? These are just examples — there are countless ideas.

As it turns out, we DO have up to 100 free microbiome profiles being offered to the QS community, thanks to Pathogenica and QS sponsor Autodesk. Now we just have to think up some cool experiments to do.

So the challenge is on – propose an experiment in the comments, and the top experiments will be done with some of the free tests. The deadline for submitting ideas is August 31. Also, all the data will be made openly available.

19 Responses to What would you do with your microbiome sequence?

At the 2012 GET Conference in late April, about 100 members of the Personal Genome Project (including myself) collected microbiome swabs (left hand, right hand, forehead, mouth, and gut) that are/will be analyzed by the Knight Lab at the University of Colorado. All of that data will be made publicly available, and will be tied to the PGP participant’s public profile. It might be interesting to use a few of the Pathogenica kits to compare the results, or to determine/analyze changes in their microbiome since then.

I’d love to study the interaction of the biome with the common cold. Including both study of individuals over time (how does the cold affect the biome) and across individuals (are some biome characteristics predictive of susceptibility to infection).

It’s great that QS members were given this opportunity to prepare some clever experiment designs and make a contribution to the studies of human bacterial flora which is a rapidly growing trend in medical sciences. Regarding the importance of microbiome in gastrointestinal health and gut-brain axis regulation (mood!), I would like to suggest you three experiments:

1. The role of nutrition. We would test the bacteria profiles in similar individuals (sex, age, health, other lifestyle factors) following different guidelines during the period of one month. We have to include the measurements before and after the chosen food regimen, as well as reduce the number of disrupting variables (for instance, supplements).

a) USDA recommendations (ChooseMyPlate.gov)
b) Typical, frankfenfood-based Western diet
c) Scientifically-justified diet: The Bulletproof Diet by Dave Asprey which is like upgraded Paleo but regarding the important role of toxins and epigenetics

I’d like to see an experiment combining data that goes into the nose with data about what goes into our mouths. It would be helpful to know how allergens or pollutants in the air increase toxicity in food/drink based on different regions of travel (so you’d have to combine it with GPS as well).

I would like to examine the relationships in my personal network (wife, son, daughter, close friends, neighbors, father, mother-in-law, brother, sister, nieces, and nephews, son’s roommates and girl friend, daughter’s roommates and boy friend, etc) and see how the microbiome is different and similar.

There are some hypotheses that the appendix acts as a reservoir for beneficial gut bacteria. Let’s sequence gut microbiomes for 50 with an without appendectomy, ideally matched for age, diet, antibiotic usage, etc.

This would be a great QS project, since it involves lots of self-experimentation, testing out consumer products, etc.

The idea is to get a bunch of people to test different armpit “treatments”, which could include use of different deodorants, washing vs no washing, rinsing with alcohol, rinsing with peroxide – you name it.

Every volunteer would treat only *one* of their armpits, using the other armpit as a built-in control (using whatever standard hygiene treatment the volunteer normally uses). After two or three weeks, we organize a sniffing party (preferably blinded) to evaluate which armpit smells least disagreeable. Notable outliers would get microbiome sequencing of both the “control” and “treatment” armpit.

Each treatment should be tested on at least three volunteers (preferably many more, depending on how many volunteers we can put together). Several rounds of treatment can be organized, as long as we leave enough time in between rounds to let the “treated” armpit adjust again to the volunteers default hygiene treatment.

I think all of these ideas could just about be accomplished if we had a central database to contribute all of our readings to that could then be analyzed statistically against known conditions of each patient/sample, against any other info anyone could dream up to gather.

I was involved in developing statistical modeling for computer network problems. Once it was trained, it’s nearly 99% accurate and rarely has a false positive. We correlated hundreds of thousands of readings (NxN correlation). This eventually led to very good models that made finding anomalies easy, then deciphering those anomalies into actionable conditions.

I always knew that this technology would be very useful in the medical field, however, I could not conceive of a viable way to collect that much uniform data. Guess what? It sounds like that part has been solved! Current hardware technology would allow data sets with millions of readings or millions of data sets with 6 figure readings to be correlated in under 24 hours.

If we could collect all of this info in a de-personalized database, thousands of people could utilize this information to discover correlates of existing conditions
that were not previously known. You will even find conditions that were uncharacterized previously (this same technique was brutally accurate at finding 0-day viruses in less than 6 minutes).

Just an idea. Why do one experiment, when we could collect all the data into one place and do many, many experiments?

I would also be interested in seeing the effect of nutrition on the microbiome sequence. However, I am curious about a comparison between a vegan diet and animal protein based diet such as Dukan’s diet. Since the microbiome sequence differs very much from person to person, it seemed to me that the experiments should be run on the same person. Instead comparing a vegan person’s sequence to a another meat eater’s sequence, it may be better to follow a person when its diet changes from vegan to meat-eating.

I have C Diff, and want to do fecal tranplant. I’m afraid to take any donor because the hazard of infecing me with something else.

1. I would like to have my microbiome sequenced before tranplant (and to see
how distrubed it is, if there is a database of healthy people I can compare).
2. If the results will pinpoint on a significant deficit in some species, then I will know I have a mission to get this species, somehow, into my gut.
3. I would to do sequencing of gut microbiome of potential donors, and to have donation from the best.

It’s generally understood that Crohn’s disease (and more generally IBD) are related to interactions between environmental, immunological and bacterial factors in genetically predisposed individuals. Both myself and my brother have the genetic disposition, I’d be really fascinated to see the results of sequencing our microbiomes and comparing them – and also performing the same for other siblings in the same situation.

Diet and the gut Flora
Hypothesis: Diet effects the diversity of gut flora.

Experiment: Assess the change in the gut flora as participants move from standard American diets to different diets. Groups to include:
o Control group (Standard American diet)
o Standard American diet no grains
o Vegetarians
o Paleo
o Other?

Procedure:
Step 1: Identify participants who are on a standard American diet and are willing to track and record what they eat via photo for 4 weeks total.
Step 2: Take pictures of diet for one week.
Step 3: Take staring microbiom measurements
Step 4: Change diets for three weeks. Record with photos.
Step 5: Take same test again.
Step 6: Compare groups and visualize data.

What to consider: Participants should not have been on antibiotics 12 months prior to test.

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